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Chemical reaction networks based on catalysis, degradation, and annihilation may be used as building blocks to construct a variety of dynamical and feedback control systems in Synthetic Biology. DNA strand-displacement, which is based on DNA hybridisation programmed using Watson-Crick base pairing, is an effective primitive to implement such reactions experimentally. However, experimental construction, validation and scale-up of nucleic acid control systems is still significantly lagging theoretical developments, due to several technical challenges, such as leakage, crosstalk, and toehold sequence design. To help the progress towards experimental implementation, we provide here designs representing two fundamental classes of reference tracking control circuits (integral and state-feedback control), for which the complexity of the chemical reactions required for implementation has been minimised. The supplied ‘minimally complex’ control circuits should be ideal candidates for first experimental validations of nucleic acid controllers.

This article presents a reconfigurable hardware/software architecture for binary acceleration of embedded applications. A Reconfigurable Processing Unit (RPU) is used as a coprocessor of the General Purpose Processor (GPP) to accelerate the execution of repetitive instruction sequences called Megablocks. A toolchain detects Megablocks from instruction traces and generates customized RPU implementations. The implementation of Megablocks with memory accesses uses a memory-sharing mechanism to support concurrent accesses to the entire address space of the GPP’s data memory. The scheduling of load/store operations and memory access handling have been optimized to minimize the latency introduced by memory accesses. The system is able to dynamically switch the execution between the GPP and the RPU when executing the original binaries of the input application. Our proof-of-concept prototype achieved geometric mean speedups of 1.60× and 1.18× for, respectively, a set of 37 benchmarks and a subset considering the 9 most complex benchmarks. With respect to a previous version of our approach, we achieved geometric mean speedup improvements from 1.22 to 1.53 for the 10 benchmarks previously used.